Do you have what it takes to interpret metabolomics? (Part 1|3)

Omics training usually covers experiment planning, data generation and collection, data preparation and analysis, but it almost always omits the last part that makes it all worthwhile: #datainterpretation

Without knowing how to make sense of your results in the broader biological context, what is the point of all this hard work?

And if you were never trained for data interpretation, how should you know how to do it? Or even if you can do it?

My experience of data interpretation has centered on understanding molecular mechanisms. Over the years, I’ve discovered that the success of any data interpretation project requires a broad set of skills that can be honed as we go. This makes the interpretation project itself a great place for those of us who love to learn. And with a little guidance, there is a lot that can be learnt on the job!

To answer the title of this post: if you really want to interpret metabolomics and you have a basic understanding of molecular biology and metabolism, you probably can. What may vary though is how much effort you'll have to put into this work in order to reach success. In short, the more experience you have with data interpretation, the easier it gets.

Over the next 3 posts I will discuss what I consider to be the 3 most important assets for data interpretation. Each of these assets makes data interpretation a personal exercise, as it relies on your own knowledge, and your own motivation.

Don’t hesitate to join me in discussing each of these in comments and let me know at the end if you would have chosen another top 3.

I will also discuss these 3 key assets and more on data interpretation in a webinar entitled The STORY principle – A 5-step guide to the biological interpretation of metabolomics. You can follow the link above to register already.

Today, we start with the obvious: bioinformatic tools, what they can and cannot do!

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Asset #1 The right tools for the job

If you have been doing this work for a while you already know: there is no single bioinformatic tool that is the best at doing anything you need for your metabolomic data interpretation. Each project will require a slightly different set of tools and/or settings to reach optimal results.

?Automated data interpretation

If you are used to working with automated workflows for sample or data processing, you may be living in hope that there is also one for data interpretation. Well... there isn’t. This makes data interpretation both beautiful and a little painful.

I often say that data interpretation is an art, which may not sit well with those aiming to describe the hard facts of biology. But this is how biology knowledge grows. We perform and analyze measurements, then interpret what the results (most likely) mean, sometimes adding a pinch of theory that remains to be proven or refuted by future works.

Happily, there are bioinformatic tools that help us get closer to our final story. The basic starting point is often the use of classical statistical methods to identify statistically significant differences between groups. More elaborate tools exist to harness the power of machine learning (data-driven tools), of pre-existing biological knowledge (biology-driven tools) or of structural similarities between metabolites (chemistry-driven tools).

Some of these tools are online software with easy-to-use graphical user interfaces, others are scripts provided by publications and research institutes. Many are free, and often come with tutorials and related publications to help us understand how best to use them.

All of them require data preparation (or pre-processing) to make sure your dataset will be properly handled. For example, you’ll need to prepare your data to remove low quality information, remove data not relevant to your analysis, add compatible identifiers, and more.

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Stay up-to-date

In my experience, the first limiting factor to using any tool is to even know of its existence. In the same way that you want to be aware of the latest technological advances for the type of instrument you use, you need to know the latest approaches to prepare and handle your data type.

Some of the best strategies to stay on top of this ever-growing list of resources are to:

• ?discuss with colleagues
• ?attend conferences and webinars
• ?screen the literature for new methods
• ?read reviews focused on bioinformatic tools

To help with this, there are people in the community generous enough to compile regularly updated lists of software and code, often shared as peer-reviewed publications. Once you find them in PubMed, don’t hesitate to set up an automated alert so you know when their next publication or review comes out. This is a great way to get regular updates on the latest developments in the field.

The worst case scenario is to discover the “perfect” tool right at the end of a project and wonder if you should repeat your work. To protect yourself from this nightmare, make sure to scour the literature for new tools that may be relevant to your research, especially at the beginning of a new project.

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Choosing the right tool

The right tool for the job satisfies multiple needs and possibilities, not all of which are scientifically “honorable.” Ideally, we would choose tools that are the best fit for our data. In real life, we must consider cost, analysis time, learning curves, influence from colleagues and other factors.

In reality, compromise is likely.

We’re also limited by our ability to run certain tools. What should you do if you have no programming skills, but your perfect tool runs exclusively on R? It may be time to ask a colleague or even the developers of the code in question if they want to collaborate and check how well the tool works for your data.

Another factor that may influence your choice is comparability. If you want to be able to compare your results with your last three studies, you might opt for the exact same analysis workflow that you used before. If a new tool or algorithm has become available since running those past studies, it may be an idea to re-run all four with that new tool, to see if this brings you more information. It may be time-consuming, but it could provide the material for another publication on a new meta-analysis of past data, in addition to your current project.

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Know your tools

A critical aspect of running any data analysis is to know what your bioinformatic tools can and cannot do and what type of data they take and don’t take. In short: know your tools’ limitations.

No tool can do everything. This is why combining several tools is often a great way to approach an interpretation project. We often run more tools than those described in the final paper. Don’t be afraid to perform many analyses if there is a sound reason to do so.

Also, whichever tool you use, imagine a warning label that reads, “no tool will write your paper for you.”

No tool will tell you exactly what’s happening in your biological system and how this fits into the larger context of biomedicine today. That’s a job for the human operating the tools.

Many tools can help identify the mechanisms that will form the core of your interpretation, but at the end of the day, you’ll still have to make the last connections yourself by studying your data and its biological context. This is where asset #2 comes in, but that’s a story for another post :)

If you are interested in learning more about data interpretation for emtabolomics, don't forget to register for my upcoming webinar in February 2023 with this link: The STORY principle – A 5-step guide to the biological interpretation of metabolomics.

#DItips #OperateYourTools #metabolomics